Advancements in autonomous vehicle (AV) technologies necessitate precise estimation of perceived risk to enhance user comfort, acceptance and trust. This paper introduces a novel AV-Occupant Risk (AVOR) model designed for perceived risk estimation during AV cut-in scenarios. An empirical study is conducted with 18 participants with realistic cut-in scenarios. Two factors were investigated: scenario risk and scene population. 76% of subjective risk responses indicate an increase in perceived risk at cut-in initiation. The existing perceived risk model did not capture this critical phenomenon. Our AVOR model demonstrated a significant improvement in estimating perceived risk during the early stages of cut-ins, especially for the high-risk scenario, enhancing modelling accuracy by up to 54%. The concept of the AVOR model can quantify perceived risk in other diverse driving contexts characterized by dynamic uncertainties, enhancing the reliability and human-centred focus of AV systems.

翻译：自动驾驶汽车（AV）技术的进步亟需对感知风险的精准估计，以提升用户舒适度、接受度与信任度。本文提出一种新型AV乘员风险（AVOR）模型，专用于AV切入场景中的感知风险估计。基于18名参与者在真实切入场景下开展实证研究，探讨了场景风险与场景人口密度两个因素。76%的主观风险响应表明，在切入起始阶段感知风险有所升高。现有感知风险模型未能捕捉这一关键现象。我们的AVOR模型在切入初期，尤其针对高风险场景，显著提升了感知风险估计精度，建模准确率最高提升54%。AVOR模型的概念可量化其他具有动态不确定性的多样化驾驶环境中的感知风险，从而增强AV系统的可靠性与以人为本的聚焦。